Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Feature relevance analysis and dimensionality reduction


The goal of feature relevance and selection is to find the features that are discriminating with respect to the target variable and help reduce the dimensions of the data [1,2,3]. This improves the model performance mainly by ameliorating the effects of the curse of dimensionality and by removing noise due to irrelevant features. By carefully evaluating models on the validation set with and without features removed, we can see the impact of feature relevance. Since the exhaustive search for k features involves 2k – 1 sets (consider all combinations of k features where each feature is either retained or removed, disregarding the degenerate case where none is present) the corresponding number of models that have to be evaluated can become prohibitive, so some form of heuristic search techniques are needed. The most common of these techniques are described next.

Feature search techniques

Some of the very common search techniques employed...